{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Port of `aes-int.py` into a Python3 module, an AES-128 analysis script initially developed by Greg d'Eon in the context of [tutorial CW305-3 (wiki)](https://wiki.newae.com/Tutorial_CW305-3_Clock_Glitching)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!/usr/bin/env python3\n",
    "\n",
    "from Crypto.Cipher import AES\n",
    "#import matplotlib.pyplot as plt\n",
    "import holoviews as hv\n",
    "hv.extension('bokeh')\n",
    "\n",
    "class AesStates():\n",
    "    def __init__(self, keysize=128, blocksize=128):\n",
    "        # ------------------------------------------------------ Parameters for AES-128\n",
    "        self.Nb = blocksize//32\n",
    "        self.Nk = keysize//32\n",
    "        self.Nr = 6 + max(self.Nb, self.Nk)\n",
    "\n",
    "    # --------------------------------------------------------------- Lookup tables\n",
    "    __s_box =     [\n",
    "        0x63, 0x7C, 0x77, 0x7B, 0xF2, 0x6B, 0x6F, 0xC5, 0x30, 0x01, 0x67, 0x2B, 0xFE, 0xD7, 0xAB, 0x76,\n",
    "        0xCA, 0x82, 0xC9, 0x7D, 0xFA, 0x59, 0x47, 0xF0, 0xAD, 0xD4, 0xA2, 0xAF, 0x9C, 0xA4, 0x72, 0xC0,\n",
    "        0xB7, 0xFD, 0x93, 0x26, 0x36, 0x3F, 0xF7, 0xCC, 0x34, 0xA5, 0xE5, 0xF1, 0x71, 0xD8, 0x31, 0x15,\n",
    "        0x04, 0xC7, 0x23, 0xC3, 0x18, 0x96, 0x05, 0x9A, 0x07, 0x12, 0x80, 0xE2, 0xEB, 0x27, 0xB2, 0x75,\n",
    "        0x09, 0x83, 0x2C, 0x1A, 0x1B, 0x6E, 0x5A, 0xA0, 0x52, 0x3B, 0xD6, 0xB3, 0x29, 0xE3, 0x2F, 0x84,\n",
    "        0x53, 0xD1, 0x00, 0xED, 0x20, 0xFC, 0xB1, 0x5B, 0x6A, 0xCB, 0xBE, 0x39, 0x4A, 0x4C, 0x58, 0xCF,\n",
    "        0xD0, 0xEF, 0xAA, 0xFB, 0x43, 0x4D, 0x33, 0x85, 0x45, 0xF9, 0x02, 0x7F, 0x50, 0x3C, 0x9F, 0xA8,\n",
    "        0x51, 0xA3, 0x40, 0x8F, 0x92, 0x9D, 0x38, 0xF5, 0xBC, 0xB6, 0xDA, 0x21, 0x10, 0xFF, 0xF3, 0xD2,\n",
    "        0xCD, 0x0C, 0x13, 0xEC, 0x5F, 0x97, 0x44, 0x17, 0xC4, 0xA7, 0x7E, 0x3D, 0x64, 0x5D, 0x19, 0x73,\n",
    "        0x60, 0x81, 0x4F, 0xDC, 0x22, 0x2A, 0x90, 0x88, 0x46, 0xEE, 0xB8, 0x14, 0xDE, 0x5E, 0x0B, 0xDB,\n",
    "        0xE0, 0x32, 0x3A, 0x0A, 0x49, 0x06, 0x24, 0x5C, 0xC2, 0xD3, 0xAC, 0x62, 0x91, 0x95, 0xE4, 0x79,\n",
    "        0xE7, 0xC8, 0x37, 0x6D, 0x8D, 0xD5, 0x4E, 0xA9, 0x6C, 0x56, 0xF4, 0xEA, 0x65, 0x7A, 0xAE, 0x08,\n",
    "        0xBA, 0x78, 0x25, 0x2E, 0x1C, 0xA6, 0xB4, 0xC6, 0xE8, 0xDD, 0x74, 0x1F, 0x4B, 0xBD, 0x8B, 0x8A,\n",
    "        0x70, 0x3E, 0xB5, 0x66, 0x48, 0x03, 0xF6, 0x0E, 0x61, 0x35, 0x57, 0xB9, 0x86, 0xC1, 0x1D, 0x9E,\n",
    "        0xE1, 0xF8, 0x98, 0x11, 0x69, 0xD9, 0x8E, 0x94, 0x9B, 0x1E, 0x87, 0xE9, 0xCE, 0x55, 0x28, 0xDF,\n",
    "        0x8C, 0xA1, 0x89, 0x0D, 0xBF, 0xE6, 0x42, 0x68, 0x41, 0x99, 0x2D, 0x0F, 0xB0, 0x54, 0xBB, 0x16\n",
    "    ]\n",
    "\n",
    "    __s_box_inv = [\n",
    "        0x52, 0x09, 0x6A, 0xD5, 0x30, 0x36, 0xA5, 0x38, 0xBF, 0x40, 0xA3, 0x9E, 0x81, 0xF3, 0xD7, 0xFB,\n",
    "        0x7C, 0xE3, 0x39, 0x82, 0x9B, 0x2F, 0xFF, 0x87, 0x34, 0x8E, 0x43, 0x44, 0xC4, 0xDE, 0xE9, 0xCB,\n",
    "        0x54, 0x7B, 0x94, 0x32, 0xA6, 0xC2, 0x23, 0x3D, 0xEE, 0x4C, 0x95, 0x0B, 0x42, 0xFA, 0xC3, 0x4E,\n",
    "        0x08, 0x2E, 0xA1, 0x66, 0x28, 0xD9, 0x24, 0xB2, 0x76, 0x5B, 0xA2, 0x49, 0x6D, 0x8B, 0xD1, 0x25,\n",
    "        0x72, 0xF8, 0xF6, 0x64, 0x86, 0x68, 0x98, 0x16, 0xD4, 0xA4, 0x5C, 0xCC, 0x5D, 0x65, 0xB6, 0x92,\n",
    "        0x6C, 0x70, 0x48, 0x50, 0xFD, 0xED, 0xB9, 0xDA, 0x5E, 0x15, 0x46, 0x57, 0xA7, 0x8D, 0x9D, 0x84,\n",
    "        0x90, 0xD8, 0xAB, 0x00, 0x8C, 0xBC, 0xD3, 0x0A, 0xF7, 0xE4, 0x58, 0x05, 0xB8, 0xB3, 0x45, 0x06,\n",
    "        0xD0, 0x2C, 0x1E, 0x8F, 0xCA, 0x3F, 0x0F, 0x02, 0xC1, 0xAF, 0xBD, 0x03, 0x01, 0x13, 0x8A, 0x6B,\n",
    "        0x3A, 0x91, 0x11, 0x41, 0x4F, 0x67, 0xDC, 0xEA, 0x97, 0xF2, 0xCF, 0xCE, 0xF0, 0xB4, 0xE6, 0x73,\n",
    "        0x96, 0xAC, 0x74, 0x22, 0xE7, 0xAD, 0x35, 0x85, 0xE2, 0xF9, 0x37, 0xE8, 0x1C, 0x75, 0xDF, 0x6E,\n",
    "        0x47, 0xF1, 0x1A, 0x71, 0x1D, 0x29, 0xC5, 0x89, 0x6F, 0xB7, 0x62, 0x0E, 0xAA, 0x18, 0xBE, 0x1B,\n",
    "        0xFC, 0x56, 0x3E, 0x4B, 0xC6, 0xD2, 0x79, 0x20, 0x9A, 0xDB, 0xC0, 0xFE, 0x78, 0xCD, 0x5A, 0xF4,\n",
    "        0x1F, 0xDD, 0xA8, 0x33, 0x88, 0x07, 0xC7, 0x31, 0xB1, 0x12, 0x10, 0x59, 0x27, 0x80, 0xEC, 0x5F,\n",
    "        0x60, 0x51, 0x7F, 0xA9, 0x19, 0xB5, 0x4A, 0x0D, 0x2D, 0xE5, 0x7A, 0x9F, 0x93, 0xC9, 0x9C, 0xEF,\n",
    "        0xA0, 0xE0, 0x3B, 0x4D, 0xAE, 0x2A, 0xF5, 0xB0, 0xC8, 0xEB, 0xBB, 0x3C, 0x83, 0x53, 0x99, 0x61,\n",
    "        0x17, 0x2B, 0x04, 0x7E, 0xBA, 0x77, 0xD6, 0x26, 0xE1, 0x69, 0x14, 0x63, 0x55, 0x21, 0x0C, 0x7D\n",
    "    ]\n",
    "\n",
    "    __r_con = [\n",
    "        0x00000000,    # Unused\n",
    "        0x01000000,    # i = 1\n",
    "        0x02000000,\n",
    "        0x04000000,\n",
    "        0x08000000,\n",
    "        0x10000000,\n",
    "        0x20000000,\n",
    "        0x40000000,\n",
    "        0x80000000,\n",
    "        0x1b000000,\n",
    "        0x36000000    # i = 10\n",
    "        ]\n",
    "\n",
    "\n",
    "    # --------------------------------------------------------------- Key expansion\n",
    "    #Apply the s-box to each of the 4 bytes in a word\n",
    "    def __sub_word(self, input):    \n",
    "        output = 0\n",
    "        for i in range(4):\n",
    "            input_byte = input & 0xFF\n",
    "            output_byte = self.__s_box[input_byte]\n",
    "            output = output + (output_byte << (8 * i))\n",
    "            input = input >> 8\n",
    "        return output\n",
    "\n",
    "    #Rotate a word, making a cyclic permutation\n",
    "    def __rot_word(self, input):    \n",
    "        return ((input << 8) + (input >> 24)) % (1 << 32)\n",
    "\n",
    "    # Perform key expansion on the provided key using the value of Nk (above)\n",
    "    def __expand_key(self, key):\n",
    "        # Split key into bytes\n",
    "        k = [key[i] for i in range(16)]\n",
    "\n",
    "        # Expand key, as per FIPS 197 section 5.2\n",
    "        w = [0 for i in range(self.Nb * (self.Nr+1))]\n",
    "        for i in range(self.Nk):\n",
    "            w[i] = ((k[4*i] << 24)\n",
    "                    + (k[4*i + 1] << 16)\n",
    "                    + (k[4*i + 2] << 8)\n",
    "                    + (k[4*i + 3] << 0))\n",
    "\n",
    "        for i in range(self.Nk, self.Nb*(self.Nr+1)):\n",
    "            temp = w[i-1]\n",
    "            if (i % self.Nk == 0):\n",
    "                temp = self.__sub_word(self.__rot_word(temp)) ^ self.__r_con[i//self.Nk]\n",
    "            w[i] = w[i-self.Nk] ^ temp\n",
    "\n",
    "        return w\n",
    "\n",
    "\n",
    "    # -------------------------------------------------------------- Cipher methods\n",
    "    # Find the polynomial x * b mod m, as defined in FIPS 197 section 4.2.1\n",
    "    def __xtime(self, b):    \n",
    "        b = (b << 1)\n",
    "        if (b & (1 << 8)):\n",
    "            b = b ^ 0x1B\n",
    "        return b & 0xFF\n",
    "\n",
    "    # XOR each column of the state with the round key\n",
    "    def __add_round_key(self, state, round_key):\n",
    "        for x in range(4):\n",
    "            w_in = ((state[0][x] << 24)\n",
    "                   + (state[1][x] << 16)\n",
    "                   + (state[2][x] << 8)\n",
    "                   + (state[3][x] << 0))\n",
    "            w_out = w_in ^ round_key[x]\n",
    "            for y in range(4):\n",
    "                state[y][x] = (w_out >> (24 - 8*y)) & 0xFF\n",
    "\n",
    "        return state\n",
    "\n",
    "    # Use the AES s-box on each byte of the current state.\n",
    "    def __sub_bytes(self, state):\n",
    "        state = [[self.__s_box[s_xy] for s_xy in s_y] for s_y in state]\n",
    "        return state\n",
    "\n",
    "    # Cyclically shift each row of the state matrix   \n",
    "    def __shift_rows(self, state):\n",
    "        output = [[0 for x in range(4)] for y in range(4)]\n",
    "        for y in range(4):\n",
    "            for x in range(4):\n",
    "                output[y][x] = state[y][(x+y) % 4]\n",
    "\n",
    "        return output\n",
    "\n",
    "    # Mix each column of the state matrix \n",
    "    def __mix_columns(self, state):\n",
    "        output = [[0 for x in range(4)] for y in range(4)]\n",
    "        for x in range(4):\n",
    "            for y in range(4):\n",
    "                output[y][x] = (self.__xtime(state[y][x])\n",
    "                               ^ self.__xtime(state[(y+1)%4][x])\n",
    "                               ^ state[(y+1)%4][x]\n",
    "                               ^ state[(y+2)%4][x]\n",
    "                               ^ state[(y+3)%4][x])\n",
    "        return output\n",
    "\n",
    "    # Convert the 4x4 state matrix into a single bytearray\n",
    "    def __stateToBytearray(self, arr):\n",
    "        ret = [arr[y][x] for x in range(4) for y in range(4)]\n",
    "        return bytearray(ret)\n",
    "\n",
    "    # Convert a bytearray into a 4x4 state matrix\n",
    "    def __bytearrayToState(self, input):\n",
    "        arr = [[0 for x in range(4)] for y in range(4)]\n",
    "        for x in range(4):\n",
    "            for y in range(4):\n",
    "                i = y + 4*x\n",
    "                arr[y][x] = int(input[i])\n",
    "        return arr\n",
    "\n",
    "    # Encrypt a 128 bit input with a 128 bit key.\n",
    "    def encryption_states(self, input, key):\n",
    "        w = self.__expand_key(key)\n",
    "\n",
    "        # Build an array of the states\n",
    "        ret = []\n",
    "\n",
    "        # Split input into bytes\n",
    "        state = self.__bytearrayToState(input)\n",
    "        ret.append(self.__stateToBytearray(state))\n",
    "\n",
    "        # Cipher\n",
    "        state = self.__add_round_key(state, w[0:self.Nb])\n",
    "        for round in range(1, self.Nr):\n",
    "            state = self.__sub_bytes(state)\n",
    "            state = self.__shift_rows(state)\n",
    "            state = self.__mix_columns(state)        \n",
    "            state = self.__add_round_key(state, w[round*self.Nb:(round+1)*self.Nb])\n",
    "            ret.append(self.__stateToBytearray(state))\n",
    "\n",
    "        state = self.__sub_bytes(state)\n",
    "        state = self.__shift_rows(state)\n",
    "        state = self.__add_round_key(state, w[self.Nr*self.Nb:(self.Nr+1)*self.Nb])\n",
    "        ret.append(self.__stateToBytearray(state))\n",
    "\n",
    "        return ret\n",
    "\n",
    "class Ref():\n",
    "    def __init__(self, intext, key, encrypt, blocksize=128):\n",
    "        self.intext=bytes(intext)\n",
    "        self.key=bytes(key)\n",
    "        self.encrypt=encrypt\n",
    "        self.aesstates = AesStates(len(self.key)*8, blocksize)\n",
    "        if encrypt:\n",
    "            self.states = self.aesstates.encryption_states(intext, key)\n",
    "        else:\n",
    "            raise NotImplemented\n",
    "\n",
    "class Glitch():\n",
    "    def __init__(self, outtext, ref):\n",
    "        self.ref = ref\n",
    "        if self.ref.encrypt:\n",
    "            # Decrypt the glitched ciphertext\n",
    "            obj = AES.new(self.ref.key, AES.MODE_ECB)\n",
    "            pt2 = obj.decrypt(bytes(outtext))\n",
    "            # Re-encrypt the glitched plaintext to get the actual states\n",
    "            self.states = self.ref.aesstates.encryption_states(pt2, self.ref.key)\n",
    "        else:\n",
    "            raise NotImplemented\n",
    "        # Calculate the differences\n",
    "        self.diffbits = []\n",
    "        self.diffbytes = []\n",
    "        for i in range(self.ref.aesstates.Nr+1):\n",
    "            self.diffbytes.append(sum([s1 ^ s2 != 0 for (s1, s2) in zip(self.ref.states[i], self.states[i])]))\n",
    "            self.diffbits.append(sum([bin(s1 ^ s2).count(\"1\") for (s1, s2) in zip(self.ref.states[i], self.states[i])]))\n",
    "\n",
    "\n",
    "class AesDiff():\n",
    "    def __init__(self, intext, key, encrypt=True, blocksize=128):\n",
    "        self.ref = Ref(intext, key, encrypt, blocksize)\n",
    "        self.glitches = []\n",
    "\n",
    "    def add_glitch(self, outtext):\n",
    "        self.glitches.append(Glitch(outtext, self.ref))\n",
    "\n",
    "    def print_refstate(self, round):\n",
    "        assert 0 >= round >= self.ref.aesstates.Nr\n",
    "        print(self.ref.states[round].hex())\n",
    "\n",
    "    def print_state(self, n, round):\n",
    "        assert n < len(self.cipherglitches)\n",
    "        assert 0 >= round >= self.ref.aesstates.Nr\n",
    "        print(self.glitches[n].states[round].hex())\n",
    "\n",
    "    def print_diff_bits(self):\n",
    "        for t in self.glitches:\n",
    "            print(\"{} => {}\".format(t.states[-1].hex(), t.diffbits))\n",
    "\n",
    "    def print_diff_bytes(self):\n",
    "        for t in self.glitches:\n",
    "            print(\"{} => {}\".format(t.states[-1].hex(), t.diffbytes))\n",
    "\n",
    "    def plot_diff_bits(self):\n",
    "        curve = None\n",
    "        for t in self.glitches:\n",
    "            if not curve:\n",
    "                curve = hv.Curve(t.diffbits)\n",
    "            else:\n",
    "                curve *= hv.Curve(t.diffbits)\n",
    "        return curve\n",
    "\n",
    "    def plot_diff_bytes(self):\n",
    "        curve = None\n",
    "        for t in self.glitches:\n",
    "            if not curve:\n",
    "                curve = hv.Curve(t.diffbytes)\n",
    "            else:\n",
    "                curve *= hv.Curve(t.diffbytes)\n",
    "        return curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examples:"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "ad=AesDiff(intext=bytes.fromhex(\"000102030405060708090a0b0c0d0e0f\"),\n",
    "           key=bytes.fromhex(\"2b7e151628aed2a6abf7158809cf4f3c\"),\n",
    "           encrypt=True)\n",
    "for c in [ \"ae6a65ee4c6937be57199e7d585c22b3\", \"501767cc2e6d32b6da0937159baf8760\", \"4f639c4609871298b6263fb1eee80e15\", \"39ec9e52f861104305dcf30e0b4d5411\", \"4f637811093320982c6d3fb12ae80e4d\", \"3a1e3f8af3fd982102b115fa6d6e272d\", \"aa0fc624db0a178dabb9b5f956836b81\", \"50fe9ecc996232b6390937e99bafec6f\", \"509f67cc876d32b6da0937fa9baf7a60\", \"50fe67c0996d95b6da8a37e943afec60\", \"50f067cc7f6d32b6da0937199bafd760\", \"cc6067ccd06d32cdda098b539bb93260\", \"3d025d025d025d025d025d025d025d02\", \"d4fe67cc996d329cda0940e99b71ec60\", \"0aeec0ccc8ee0525fc7168ba135f0ebc\", \"50fec8cc998232b6620937e99bafeca3\"\n",
    " ]:\n",
    "    ad.add_glitch(bytes.fromhex(c))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "ad.print_diff_bits()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "ae6a65ee4c6937be57199e7d585c22b3 => [66, 64, 60, 61, 65, 67, 69, 65, 52, 58, 54]\n",
    "501767cc2e6d32b6da0937159baf8760 => [69, 66, 68, 60, 65, 58, 55, 19, 4, 19, 22]\n",
    "4f639c4609871298b6263fb1eee80e15 => [59, 62, 68, 68, 52, 60, 67, 51, 43, 64, 63]\n",
    "39ec9e52f861104305dcf30e0b4d5411 => [69, 64, 76, 70, 69, 61, 67, 29, 30, 70, 65]\n",
    "4f637811093320982c6d3fb12ae80e4d => [57, 55, 64, 58, 58, 62, 68, 54, 39, 70, 63]\n",
    "3a1e3f8af3fd982102b115fa6d6e272d => [72, 63, 60, 67, 62, 68, 75, 58, 65, 52, 59]\n",
    "aa0fc624db0a178dabb9b5f956836b81 => [66, 67, 57, 67, 52, 58, 69, 32, 20, 73, 59]\n",
    "50fe9ecc996232b6390937e99bafec6f => [70, 69, 62, 71, 61, 71, 65, 15, 3, 17, 19]\n",
    "509f67cc876d32b6da0937fa9baf7a60 => [61, 61, 69, 74, 69, 59, 75, 14, 3, 21, 14]\n",
    "50fe67c0996d95b6da8a37e943afec60 => [70, 68, 64, 60, 76, 64, 59, 16, 3, 22, 14]\n",
    "50f067cc7f6d32b6da0937199bafd760 => [73, 54, 68, 66, 65, 75, 68, 20, 4, 15, 17]\n",
    "cc6067ccd06d32cdda098b539bb93260 => [69, 68, 65, 55, 67, 71, 58, 17, 11, 36, 37]\n",
    "3d025d025d025d025d025d025d025d02 => [61, 59, 64, 64, 64, 62, 61, 65, 59, 69, 72]\n",
    "d4fe67cc996d329cda0940e99b71ec60 => [61, 68, 65, 63, 69, 77, 71, 13, 3, 10, 17]\n",
    "0aeec0ccc8ee0525fc7168ba135f0ebc => [63, 70, 66, 60, 69, 53, 65, 31, 25, 56, 57]\n",
    "50fec8cc998232b6620937e99bafeca3 => [64, 58, 57, 57, 61, 68, 67, 14, 4, 17, 21]\n",
    "```"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "ad.plot_diff_bits()"
   ]
  },
  {
   "attachments": {
    "download1.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![download1.png](attachment:download1.png)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "ad.print_diff_bytes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "ae6a65ee4c6937be57199e7d585c22b3 => [16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16]\n",
    "501767cc2e6d32b6da0937159baf8760 => [16, 16, 16, 16, 16, 16, 16, 4, 1, 4, 4]\n",
    "4f639c4609871298b6263fb1eee80e15 => [16, 16, 16, 16, 16, 16, 16, 12, 10, 16, 16]\n",
    "39ec9e52f861104305dcf30e0b4d5411 => [16, 16, 16, 16, 16, 16, 16, 8, 7, 16, 16]\n",
    "4f637811093320982c6d3fb12ae80e4d => [16, 15, 16, 16, 16, 16, 16, 12, 9, 16, 16]\n",
    "3a1e3f8af3fd982102b115fa6d6e272d => [16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16]\n",
    "aa0fc624db0a178dabb9b5f956836b81 => [16, 16, 16, 16, 16, 16, 16, 8, 5, 16, 16]\n",
    "50fe9ecc996232b6390937e99bafec6f => [16, 16, 16, 16, 16, 16, 16, 4, 1, 4, 4]\n",
    "509f67cc876d32b6da0937fa9baf7a60 => [16, 16, 16, 16, 16, 16, 16, 4, 1, 4, 4]\n",
    "50fe67c0996d95b6da8a37e943afec60 => [16, 16, 16, 16, 16, 16, 16, 4, 1, 4, 4]\n",
    "50f067cc7f6d32b6da0937199bafd760 => [16, 15, 16, 16, 16, 16, 16, 4, 1, 4, 4]\n",
    "cc6067ccd06d32cdda098b539bb93260 => [16, 16, 16, 16, 16, 16, 16, 4, 2, 8, 8]\n",
    "3d025d025d025d025d025d025d025d02 => [16, 15, 16, 16, 16, 16, 16, 16, 16, 16, 16]\n",
    "d4fe67cc996d329cda0940e99b71ec60 => [16, 16, 16, 16, 16, 16, 16, 4, 1, 4, 4]\n",
    "0aeec0ccc8ee0525fc7168ba135f0ebc => [16, 16, 16, 16, 16, 15, 16, 8, 6, 15, 15]\n",
    "50fec8cc998232b6620937e99bafeca3 => [16, 16, 16, 16, 16, 16, 16, 4, 1, 4, 4]\n",
    "```"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "ad.plot_diff_bytes()"
   ]
  },
  {
   "attachments": {
    "download2.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![download2.png](attachment:download2.png)"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python",
   "pygments_lexer": "ipython3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
